The present invention generally relates to search and analysis of electronic medical record data. More particularly, the present invention relates to refining the identification of clinical study candidates based on electronic medical record data.
Many aspects of the healthcare industry are becoming increasingly electronic in nature. Hospitals typically utilize computer systems to manage the various departments within a hospital and data about each patient is collected by a variety of computer systems. For example, a patient may be admitted to the hospital for a Transthoracic Echo (TTE). Information about the patient (e.g., demographics and insurance) could be obtained by the hospital information system (HIS) and stored on a patient record. This information could then be passed to the cardiology department system (commonly known as the cardio vascular information system, or CVIS), for example. Typically the CVIS is a product of one company, while the HIS is the product of another company. As a result, the database between the two may be different. Further, information systems may capture/retain and send different levels of granularity in the data. Once the patient information has been received by the CVIS, the patient may be scheduled for a TTE in the echo lab. Next, the TTE is performed by the sonographer. Images and measurements are taken and sent to the CVIS server. The reading physician (e.g., an echocardiographer) sits down at a review station and pulls the patient's TTE study. The echocardiographer then begins to review the images and measurements and creates a complete medical report on the study. When the echocardiographer completes the medical report, the report is sent to the CVIS server where it is stored and associated with the patient through patient identification data. This completed medical report is an example of the kind of report that could be sent to a data repository for public data mining. Medication instructions, such as documentation and/or prescription, as well as laboratory results and/or vital signs, may also be generated electronically and saved in a data repository.
Today, medical device manufacturers and drug companies face an ever-growing challenge in collecting clinical data on the real-life utilization of their products. As patient medical reports are becoming computerized, the ability to obtain real-life utilization data becomes easier. Further, the data is easier to combine and analyze (e.g., mine) for greater amounts of useful information.
As medical technology becomes more sophisticated, clinical analysis may also become more sophisticated. Increasing amounts of data are generated and archived electronically. With the advent of clinical information systems, a patient's history may be available at a touch of a button. While accessibility of information is advantageous, time is a scarce commodity in a clinical setting. To realize a full benefit of medical technological growth, it would be highly desirable for clinical information to be organized and standardized.
Even if clinical or image-related information is organized, current systems often organize data in a format determined by developers that is unusable by one or more medical practitioners in the field. Additionally, information may be stored in a format that does not lend itself to data retrieval and usage in other contexts. Thus, a need exists to structure data and instructions in a way that is easier to comprehend and utilize.
Data warehousing methods have been used to aggregate, clean, stage, report and analyze patient information derived from medical claims billing and electronic medical records (EMR). Patient data may be extracted from multiple EMR databases located at patient care provider (PCP) sites in geographically dispersed locations, then transported and stored in a centrally located data warehouse. The central data warehouse may be a source of information for population-based profile reports of physician productivity, preventative care, disease-management statistics and research on clinical outcomes. Patient data is sensitive and confidential, and therefore, specific identifying information must be removed prior to transporting it from a PCP site to a central data warehouse. This removal of identifying information must be performed per the federal Health Insurance Portability and Accountability Act (HIPAA) regulations. Any data that is contained in a public database must not reveal the identity of the individual patients whose medical information is contained in the database. Because of this requirement, any information contained on a medical report or record that could aid in tracing back to a particular individual must be removed from the report or record prior to adding the data to a data warehouse for public data mining.
Patient data may be useful to medical advancement, as well as diagnosis and treatment of patients, in a variety of ways. In order to accurately assess the impact of a particular drug or treatment on a patient, for example, it is helpful to analyze all medical reports relating to the particular patient. Removing data that can be used to trace back to an individual patient can make it impossible to group and analyze all medical reports relating to a particular patient. In addition, one of the aims of population analysis is to assemble an at-risk cohort population comprised of individuals who may be candidates for clinical intervention. De-identified data is not very useful to the patient care providers who need to know the identity of their own patients in order to treat them. Users of the system may need the ability to re-identify patients for further follow-up. Portal users may need to re-identify the patients in a process that doesn't involve the portal system, i.e. the process of re-identification occurs on the local user's system.
One avenue for medical advancement occurs through administration of clinical studies. Current identification of clinical study participants typically involves the use of mass media to broadcast a need for patients who fit a list of clinical conditions that would potentially qualify the candidate for clinical study. This manual, mass media-based selection process is lengthy and expensive due to the cost of using mass media and the time involving in crafting a message, broadcasting the message, and waiting for responses. Thus, systems and methods for identifying potential clinical study participants more rapidly would be highly desirable. Systems and methods for identifying potential clinical study participants with greater precision and less expense would also be highly desirable.
Currently, researchers design clinical study protocols using recent statistics on disease incidence and published literature on similar studies to best define clinical conditions or parameters that will yield a potential study pool of significant size and quality. Such protocol design is a ‘trial and error’ methodology. Use of trial and error protocol design methodologies involve great expense when a certain protocol has begun recruitment and requires alteration due to insignificant potential study participant volume, for example. Often, changes to the protocol require that all previously qualified patients must be re-screened based on the latest study parameters. Thus, systems and methods for improved adjustment of clinical study protocols and screening of potential clinical study participants would be highly desirable.
Using current methods, clinical study investigators are recruited for participation during the early phase of a study using a variety of methods, including mass media, databases, and word of mouth. The goal of the recruitment effort is to identify researchers with a clinical and research background who meet the study criteria and who also service a patient population large and focused enough to contain patients whose clinical backgrounds meet the study criteria. Often, a clinical study sponsor will phone clinical study investigators to gauge both interest and an estimate for the number of patients who meet study criteria. Frequently, this method of self-reporting over estimates the number of potential study candidates because these estimates are attained from the investigator's memory versus an actual patient medical record. By over-estimating the patient pool, the study may not meet its end-targets, may not reach statistical significance, may incur major expenses to urgently find more participants, may incur significant delays, and/or may require a total change of the study protocol and thus re-recruitment of participants.
Therefore, there is a need for systems and methods for improved clinical study definition and participant selection. There is a need for systems and methods for improved clinical trial configuration in compliance with HIPAA.